Bringing Direct Bolt Preload Measurement to the Aerospace Industry
Bibliographic record
Abstract
Many helicopter components are held together with fastened joints that include threaded bolts. Bolt preload is important for keeping fastened joints from loosening or sliding. In the aerospace industry, bolt preload is typically set by applying a specified torque. Common procedures for clamping a joint include the use of a calibrated torque wrench to apply the specified torque. While a torque wrench will display the torque applied to a clamped bolt assembly, the preload (or bolt tension) must be inferred. However, a significant factor relating applied torque to the acquired bolt tension is the friction between the bolt threads and joint interface. Tension measurement techniques are available in the aerospace industry, but they are not common. A small amount of contamination or lubricant can significantly alter the torque-tension relationship. Under the Future Advanced Rotorcraft Drive Systems (FARDS) agreement, Bell Helicopter, IntellifastTM, and the Army Aviation Development Directorate (ADD) - Aviation Applied Technology Directorate (AATD) developed and tested a technology that directly measures the tension in the bolted flexure joints of a KAFLEX drive shaft coupling. The main purpose of testing was to validate bolt tension measurement to demonstrate the Technology Readiness Level (TRL) suitable for flight test. Testing performed under the FARDS agreement for this new technology included measuring the variation in bolt preload when set to a specific torque and then dynamically testing the joint to simulate flight time. Correlation was obtained between joint movement and the reduction in bolt preload. This paper summarizes the test results and shows the importance of directly measuring bolt preload in a fastened assembly to ensure joint integrity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".